In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy

print("✅ All libraries loaded.")
print("NumPy:", np.__version__)
print("Pandas:", pd.__version__)
print("Seaborn:", sns.__version__)
print("SciPy:", scipy.__version__)

✅ All libraries loaded.
NumPy: 1.26.4
Pandas: 2.3.0+4.g1dfc98e16a
Seaborn: 0.11.2
SciPy: 1.13.1
In [2]:
df = pd.read_csv('Food_Delivery_Times.csv')
df.head()
Out[2]:
Order_ID Distance_km Weather Traffic_Level Time_of_Day Vehicle_Type Preparation_Time_min Courier_Experience_yrs Delivery_Time_min
0 522 7.93 Windy Low Afternoon Scooter 12 1.0 43
1 738 16.42 Clear Medium Evening Bike 20 2.0 84
2 741 9.52 Foggy Low Night Scooter 28 1.0 59
3 661 7.44 Rainy Medium Afternoon Scooter 5 1.0 37
4 412 19.03 Clear Low Morning Bike 16 5.0 68
In [3]:
# viewing dataset info 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 9 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   Order_ID                1000 non-null   int64  
 1   Distance_km             1000 non-null   float64
 2   Weather                 970 non-null    object 
 3   Traffic_Level           970 non-null    object 
 4   Time_of_Day             970 non-null    object 
 5   Vehicle_Type            1000 non-null   object 
 6   Preparation_Time_min    1000 non-null   int64  
 7   Courier_Experience_yrs  970 non-null    float64
 8   Delivery_Time_min       1000 non-null   int64  
dtypes: float64(2), int64(3), object(4)
memory usage: 70.4+ KB
In [4]:
df.shape
Out[4]:
(1000, 9)
In [5]:
# check for duplicated rows 
df.duplicated().sum()
Out[5]:
0
In [6]:
df['Traffic_Level'].value_counts()
Out[6]:
Traffic_Level
Medium    390
Low       383
High      197
Name: count, dtype: int64
In [7]:
df['Weather'].value_counts()
Out[7]:
Weather
Clear    470
Rainy    204
Foggy    103
Snowy     97
Windy     96
Name: count, dtype: int64
In [8]:
df.describe()
Out[8]:
Order_ID Distance_km Preparation_Time_min Courier_Experience_yrs Delivery_Time_min
count 1000.000000 1000.000000 1000.000000 970.000000 1000.000000
mean 500.500000 10.059970 16.982000 4.579381 56.732000
std 288.819436 5.696656 7.204553 2.914394 22.070915
min 1.000000 0.590000 5.000000 0.000000 8.000000
25% 250.750000 5.105000 11.000000 2.000000 41.000000
50% 500.500000 10.190000 17.000000 5.000000 55.500000
75% 750.250000 15.017500 23.000000 7.000000 71.000000
max 1000.000000 19.990000 29.000000 9.000000 153.000000
In [ ]:
  • Exploration Summary
  • we have a dataframe consisting of 1000 rows and 9 columns.
  • database have some null values , we need to remove them.
  • we can normalize the text like 'clear', 'Clear', 'CLEAR'.
  • Convert categorical columns to category type (for memory efficiency).

Data Cleaning¶

In [9]:
df.isna().sum()
Out[9]:
Order_ID                   0
Distance_km                0
Weather                   30
Traffic_Level             30
Time_of_Day               30
Vehicle_Type               0
Preparation_Time_min       0
Courier_Experience_yrs    30
Delivery_Time_min          0
dtype: int64
In [10]:
# dropping NaNs 
df.dropna(inplace = True)
# confirming 
df.isna().sum()
Out[10]:
Order_ID                  0
Distance_km               0
Weather                   0
Traffic_Level             0
Time_of_Day               0
Vehicle_Type              0
Preparation_Time_min      0
Courier_Experience_yrs    0
Delivery_Time_min         0
dtype: int64
In [11]:
df.duplicated().sum()
Out[11]:
0
In [12]:
df.isnull().sum()
Out[12]:
Order_ID                  0
Distance_km               0
Weather                   0
Traffic_Level             0
Time_of_Day               0
Vehicle_Type              0
Preparation_Time_min      0
Courier_Experience_yrs    0
Delivery_Time_min         0
dtype: int64
In [13]:
df.dtypes
Out[13]:
Order_ID                    int64
Distance_km               float64
Weather                    object
Traffic_Level              object
Time_of_Day                object
Vehicle_Type               object
Preparation_Time_min        int64
Courier_Experience_yrs    float64
Delivery_Time_min           int64
dtype: object
In [14]:
categorical_columns = ['Weather', 'Traffic_Level', 'Time_of_Day', 'Vehicle_Type']

for col in categorical_columns:
    df[col] = df[col].str.strip().str.lower().str.title()
In [15]:
for col in categorical_columns:
    df[col] = df[col].astype('category')
In [16]:
# viewing dataset info 
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 883 entries, 0 to 999
Data columns (total 9 columns):
 #   Column                  Non-Null Count  Dtype   
---  ------                  --------------  -----   
 0   Order_ID                883 non-null    int64   
 1   Distance_km             883 non-null    float64 
 2   Weather                 883 non-null    category
 3   Traffic_Level           883 non-null    category
 4   Time_of_Day             883 non-null    category
 5   Vehicle_Type            883 non-null    category
 6   Preparation_Time_min    883 non-null    int64   
 7   Courier_Experience_yrs  883 non-null    float64 
 8   Delivery_Time_min       883 non-null    int64   
dtypes: category(4), float64(2), int64(3)
memory usage: 45.5 KB
In [17]:
df.corr(numeric_only=True)
Out[17]:
Order_ID Distance_km Preparation_Time_min Courier_Experience_yrs Delivery_Time_min
Order_ID 1.000000 -0.010197 -0.029740 0.003005 -0.012632
Distance_km -0.010197 1.000000 -0.025518 -0.003372 0.783226
Preparation_Time_min -0.029740 -0.025518 1.000000 -0.030776 0.296529
Courier_Experience_yrs 0.003005 -0.003372 -0.030776 1.000000 -0.076787
Delivery_Time_min -0.012632 0.783226 0.296529 -0.076787 1.000000
In [18]:
df.head()
Out[18]:
Order_ID Distance_km Weather Traffic_Level Time_of_Day Vehicle_Type Preparation_Time_min Courier_Experience_yrs Delivery_Time_min
0 522 7.93 Windy Low Afternoon Scooter 12 1.0 43
1 738 16.42 Clear Medium Evening Bike 20 2.0 84
2 741 9.52 Foggy Low Night Scooter 28 1.0 59
3 661 7.44 Rainy Medium Afternoon Scooter 5 1.0 37
4 412 19.03 Clear Low Morning Bike 16 5.0 68

Data Visualisation¶

In [19]:
# setting up seaborn configurations 
sns.set_style('whitegrid')

Q1. How does weather affect delivery time?¶

In [20]:
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10,5))
sns.barplot(x='Weather', y='Delivery_Time_min', data=df)
plt.title("Impact of Weather on Delivery Time")
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:253: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
  • Delivery time increases due to Snowy weather.

Q2. Is delivery time different in high traffic conditions?¶

In [21]:
sns.barplot(x='Traffic_Level', y='Delivery_Time_min', data=df)
plt.title("Traffic vs Delivery Time")
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:253: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
  • Yes, the Delivery time increases due to High traffic.

Q3. Which vehicle type is fastest for delivery?¶

In [22]:
sns.barplot(x='Vehicle_Type', y='Delivery_Time_min', data=df)
plt.title("Delivery Time by Vehicle Type")

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:253: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
Out[22]:
Text(0.5, 1.0, 'Delivery Time by Vehicle Type')
  • Scooter is fastest for the delivery.

Q4. Does courier experience reduce delivery delay?¶

In [23]:
import plotly.express as px

# Group and round the average
df_mean = df.groupby('Courier_Experience_yrs')['Delivery_Time_min'].mean().reset_index()
df_mean = df_mean.round(2)
df_mean = df_mean.sort_values(by='Courier_Experience_yrs')

fig = px.bar(df_mean,
             x='Delivery_Time_min',
             y='Courier_Experience_yrs',
             orientation='h',
             text='Delivery_Time_min',
             color='Delivery_Time_min',
             color_continuous_scale='Blues')

fig.update_layout(
    title="Experience vs Delivery Time",
    xaxis_title="Avg Delivery Time (min)",
    yaxis_title="Courier Experience (Years)",
    yaxis=dict(tickmode='linear', tick0=0, dtick=1) 
)

fig.show()
  • Yes, the drivers who have experience more than 4 years have less delivery time.

Q5. Which time of day causes most delay?¶

In [24]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.barplot(x='Time_of_Day', y='Delivery_Time_min', data=df)
plt.title("Time_of_Day Impact on Delivery Time")
plt.ylabel("Delivery_Time_min")
plt.xlabel("Time_of_Day")
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:253: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  • Evening time of the day causes the most delay.

Q6. Which factor has the strongest effect on delivery time?¶

In [25]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

# 1. Copy Data
df_model = df.copy()

# 2. Encode categorical columns
cat_cols = df_model.select_dtypes(include=['category']).columns
df_model[cat_cols] = df_model[cat_cols].apply(LabelEncoder().fit_transform)

# 3. Features and Target
X = df_model.drop(['Order_ID', 'Delivery_Time_min'], axis=1)
y = df_model['Delivery_Time_min']

# 4. Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 5. Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# 6. Feature Importance
importances = model.feature_importances_
features = X.columns

# 7. Visualize
importance_df = pd.DataFrame({'Feature': features, 'Importance': importances}).sort_values(by='Importance', ascending=False)

sns.barplot(x='Importance', y='Feature', data=importance_df)
plt.title("Feature Importance for Delivery Time Prediction")
plt.show()
  • Distance factor has the most empact on the Delivery time.

Q7. Make future delivery time prediction using ML model.¶

In [26]:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

df_ml = df.copy()

# Encode all categorical features
cat_cols = df_ml.select_dtypes(include='category').columns
le_dict = {}  # Save encoders to use later

for col in cat_cols:
    le = LabelEncoder()
    df_ml[col] = le.fit_transform(df_ml[col])
    le_dict[col] = le

# Features and target
X = df_ml.drop(['Order_ID', 'Delivery_Time_min'], axis=1)
y = df_ml['Delivery_Time_min']

# Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# values(optional)
custom_input = {
    'Distance_km': 10,
    'Weather': le_dict['Weather'].transform(['Windy'])[0],
    'Traffic_Level': le_dict['Traffic_Level'].transform(['Medium'])[0],
    'Time_of_Day': le_dict['Time_of_Day'].transform(['Morning'])[0],
    'Vehicle_Type': le_dict['Vehicle_Type'].transform(['Scooter'])[0],
    'Preparation_Time_min': 20,
    'Courier_Experience_yrs': 8.0
}

# Convert to DataFrame
input_df = pd.DataFrame([custom_input])

predicted_time = model.predict(input_df)
print(f"📦 Estimated Delivery Time: {predicted_time[0]:.2f} minutes")

📦 Estimated Delivery Time: 60.36 minutes
In [ ]:

Conclusion – Summary Report¶

1. How does weather affect delivery time?¶

Delivery time increases during Snowy and Foggy weather conditions.¶

2. Is delivery time different in high traffic conditions?¶

Yes, delivery time tends to increase during high traffic.¶

3. Which vehicle type is fastest for delivery?¶

Among the available options, Scooter was observed to be the fastest vehicle type on average for deliveries.¶

4. Does courier experience reduce delivery delay?¶

Yes, delivery time decreases as courier experience increases. Couriers with more than 4 years of experience generally deliver faster than less experienced ones.¶

5. Which time of day causes most delay?¶

Deliveries during the Evening show the highest average delivery time.¶

6. Which factor has the strongest effect on delivery time?¶

Among all variables, Distance has the strongest correlation with delivery time. Longer distances naturally result in longer delivery durations.¶

Suggestions to Reduce Delivery Time¶

Optimize route selection using real-time traffic data¶

Use GPS APIs to guide drivers through less congested routes.¶

Assign more cooks to reduce Preparation time.¶

Open more small branches so that nearer branch of the order will be acceccible more quickly.¶

Assign experienced couriers (>4 years) for longer distance or bad weather deliveries. Experience leads to better time management and faster decision-making.¶

In [ ]: